is a system which is able to detect network hydraulic anomalies – usually developing blockages - before they cause flooding other causes (FOC) or pollution events. It is low power, low maintenance and low cost, with web-access via a visual alarm-handling platform. The system dramatically increases observability, and promotes responsive rather than reactive management by the operator. The operator is able to respond to potential and developing blockages before they impact on their customers and performance figures.
The system has been developed by EMS with part-funding from Innovate UK. Components of SMART Sewer™
have been developed in the modelling environment, the workshop and the laboratory. It is now ready for field deployment and offers a good fit with Water Companies’ oncoming AMP6 requirements.
SMART Sewer™ enables a cause to be identified as it develops such that cleaning interventions can be tightly targeted in a timely manner.
Currently, sewer cleaning is reactive or blanket-style prioritised using a risk-based approach (risk taken as the product of probability and consequence; consequence based on hydraulic models). Generally, the only alarm available regarding FOC events is from customers to report that they’ve been flooded.
CSO monitors have historically had limited success in detecting blockages. They are not designed or targeted in the network for this. They are not designed for widespread deployment with their relatively high cost of ownership.
SMART Sewer™ operates with little maintenance. Low-cost in-sewer RTUs with level monitors can be widely deployed; battery life in the chosen mode of operation should be 5 years. GPRS communication is used to transfer data for processing by an Analytics Engine in order to determine the location of possible developing blockages.
Manhole level information is analysed using intelligent routines which are the crux of the system. This allows the identification of changes in intervening pipes and alarms to be flagged on the SMART Sewer™ platform. Using this approach, SMART Sewer™ can derive information from both dry and wet weather behaviour and differentiate between the two.
The Analytics Engine is a Fuzzy Logic Inference System (FLIS). Membership Functions capture the likelihood that blockages are present in intervening pipes based on relative levels. Outputs are based on a RAG system where the 3 grades of blockage probability are 0 to 0.3 (clean), 0.3 to 0.6 (partially blocked), 0.6 to 1.0 (blocked).
This combination of physical phenomena and method of evaluation are unique and are the subject of a UK Provisional Patent Application.
The analytical routine will pick up blockage signatures in dry weather or wet weather flow. The daily rising and recession limbs of dry weather flow render the most useful information. It may be that level is recorded and transmitted only in these time-windows and during rainfall events.
The business case for deployment can only be assessed on a case-by-case basis. Within some regimes, more widespread deployment may be logical. Within others, tightly targeted deployment may give a return.
The deployment of SMART Sewer™ should be thought of in the general risk – probability times consequence – framework.
We anticipate that SMART Sewer™ will be most suitably deployed in “hot-spot” areas where the consequences of blockage events are significant, i.e. we don’t anticipate the blanket deployment of the system.
For example, regulatory incentives around FOC events in the UK can be very high. At the point of missing FOC targets within SIM scores, each FOC event can be worth more than £100k.
In central areas of cities, access may be difficult and costly, and consequences of escapes due to blockages may be very significant. This heightens the need for SMART Sewer™.
Where cleaning costs are high then the displacement of cleaning activity should be considered. It should also be considered that FOC events are increasingly viewed badly within developed society.
For more information on the project please contact the EMS Innovation Manager Dr Sonja Ostojin or call 0114 272 2270.